Learning Patterns of Latent Residual for Improving Video Compression
Abstract
We tackle the problem of reducing compression artifacts. Specifically, we focus on transmitting the residual from the original video, i.e. difference between a compressed video and its corresponding original/uncompressed one, together with the compressed video during video transmission. Our video compression pipeline is capable of diminishing the overall cost of transmitting the residual and simultaneously achieving comparable video quality with respect to a state-of-the-art baseline. We provide experimental results on several datasets, including the one with great diversity, to substantiate the capacity of our pipeline in improving video compression.
Cite
Text
Chen et al. "Learning Patterns of Latent Residual for Improving Video Compression." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.Markdown
[Chen et al. "Learning Patterns of Latent Residual for Improving Video Compression." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/chen2019cvprw-learning/)BibTeX
@inproceedings{chen2019cvprw-learning,
title = {{Learning Patterns of Latent Residual for Improving Video Compression}},
author = {Chen, Yen-Chung and Chang, Keng-Jui and Tsai, Yi-Hsuan and Chiu, Wei-Chen},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
url = {https://mlanthology.org/cvprw/2019/chen2019cvprw-learning/}
}